point

 

 Remember me

Register  |   Lost password?










All site blogs

Blog Post: HighFrequencyTradingReview: Dynamical Models of Market Impact and Algorithms for Order Execution [Gatheral, Schied]

May 4, 2012 by MoneyScience   Comments (0)

Abstract: In this review article, we present recent work on the regularity of dynamical market impact models and their associated optimal order execution strategies. In particular, we address the question of the stability and existence of optimal strategies, showing that in a large class of models, there is price manipulation and no well-behaved optimal order execution strategy. We also address issues arising from the use of dark pools and predatory trading.read more...

Blog Post: WealthandCapitalMarketsBlog: Replacing the poster child of failure?

May 4, 2012 by MoneyScience   Comments (0)

Yesterday, the Bank of International Settlements released consultative document sets out a revised market risk framework that proposes a number of specific measures to improve trading book capital requirements. (BIS Fundamental Review of the Trading Book)read more...

, , , , , , ,

Dynamical Models of Market Impact and Algorithms for Order Execution [Gatheral, Schied]

May 4, 2012 by mikeohara   Comments (0)

Abstract:
In this review article, we present recent work on the regularity of dynamical market impact models and their associated optimal order execution strategies. In particular, we address the question of the stability and existence of optimal strategies, showing that in a large class of models, there is price manipulation and no well-behaved optimal order execution strategy. We also address issues arising from the use of dark pools and predatory trading.

read more...

, , ,

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading [Ho, Liu, Yu]

May 4, 2012 by mikeohara   Comments (0)

Abstract:
This study models the role of public news arrivals on asset correlation in a trading environment populated by computerized algorithms. The model is empirically tested with the individual stock futures and its underlying spot markets, which are characterized by the mechanical cost-of-carry relation that is typically exploited by algorithmic trading. In normal circumstances, the return correlation between the stock futures and spot quotes is nearly perfect, because futures market makers peg their quotes to those of the underlying by using computerized algorithms. Our simple model predicts that this near-perfect correlation can occasionally break down with two conditions: one, the futures market is less liquid than the corresponding spot market; and two, the uncertainty surrounding the impact of the news on the underlying stocks is sufficiently large. This breakdown occurs because the futures market makers switch from automating the quote-matching process to manually monitor and update their quotes. By employing the comprehensive RavenPack database with firm-level news releases, we test and confirm our model predictions. In particular, the spot-futures return correlation falls as the news uncertainty rises, and this correlation breakdown is more prominent for small-cap stocks. Furthermore, for actively traded stocks, the impact of the news on the breakdown is more intense. If the overall stock market experiences extreme turbulence, however, this impact is weaker. We discuss the implications for the limits of algorithmic trading.

read more...

, , , , , , , ,